A Novel Particle Swarm-based Method for Nonlinear Function Optimization

نویسندگان

  • X. Wang
  • S. J. Ovaska
چکیده

This paper proposes a hybrid Particle Swarm Optimization (PSO) method, which is based on the fusion of the PSO, Clonal Selection Algorithm (CSA), and Mind Evolutionary Computation (MEC). The clone function borrowed from the CSA and MEC-characterized similartaxis and dissimilation operations are embedded in the original PSO algorithm. Simulations of nonlinear function optimization are made to compare this hybrid PSO with the regular PSO method. It has been demonstrated that our hybrid optimization algorithm can achieve a better convergence performance, and provide diverse solutions to the multi-model optimization problems.

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Research of Blind Signals Separation with Genetic Algorithm and Particle Swarm Optimization Based on Mutual Information

Blind source separation technique separates mixed signals blindly without any information on the mixing system. In this paper, we have used two evolutionary algorithms, namely, genetic algorithm and particle swarm optimization for blind source separation. In these techniques a novel fitness function that is based on the mutual information and high order statistics is proposed. In order to evalu...

متن کامل

Research of Blind Signals Separation with Genetic Algorithm and Particle Swarm Optimization Based on Mutual Information

Blind source separation technique separates mixed signals blindly without any information on the mixing system. In this paper, we have used two evolutionary algorithms, namely, genetic algorithm and particle swarm optimization for blind source separation. In these techniques a novel fitness function that is based on the mutual information and high order statistics is proposed. In order to evalu...

متن کامل

Optimization of the Lyapunov Based Nonlinear Controller Parameters in a Single-Phase Grid-Connected Inverter

In this paper, optimization of the backstepping controller parameters in a grid-connected single-phase inverter is studied using Imperialist competitive algorithm (ICA), Genetic Algorithm (GA) and Particle swarm optimization (PSO) algorithm. The controller is developed for the system based on state-space averaged model. By selection of a suitable Lyapunov function, stability of the proposed con...

متن کامل

An Improved Particle Swarm Optimizer Based on a Novel Class of Fast and Efficient Learning Factors Strategies

The particle swarm optimizer (PSO) is a population-based metaheuristic optimization method that can be applied to a wide range of problems but it has the drawbacks like it easily falls into local optima and suffers from slow convergence in the later stages. In order to solve these problems, improved PSO (IPSO) variants, have been proposed. To bring about a balance between the exploration and ex...

متن کامل

Harmonics Estimation in Power Systems using a Fast Hybrid Algorithm

In this paper a novel hybrid algorithm for harmonics estimation in power systems is proposed. The estimation of the harmonic components is a nonlinear problem due to the nonlinearity of phase of sinusoids in distorted waveforms. Most researchers implemented nonlinear methods to extract the harmonic parameters. However, nonlinear methods for amplitude estimation increase time of convergence. Hen...

متن کامل

Non-linear Fractional-Order Chaotic Systems Identification with Approximated Fractional-Order Derivative based on a Hybrid Particle Swarm Optimization-Genetic Algorithm Method

Although many mathematicians have searched on the fractional calculus since many years ago, but its application in engineering, especially in modeling and control, does not have many antecedents. Since there are much freedom in choosing the order of differentiator and integrator in fractional calculus, it is possible to model the physical systems accurately. This paper deals with time-domain id...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

عنوان ژورنال:

دوره   شماره 

صفحات  -

تاریخ انتشار 2009